Your Benchmark Is an Empirical Measure Over DifficultyThe paper treats a benchmark as an empirical measure over item difficulty and formalizes two failure modes: measure mismatch (benchmark difficulty distribution differs from the intended one) and coverage gaps (uncovered difficulty regions that hide localized training progress). It proposes two diagnostic metrics with theoretical guarantees and analyzes 25 popular benchmarks across 7 domains using estimated item difficulties. It also introduces BenchFill, a difficulty-targeted item rewriting algorithm that reduces local coverage gaps.
huggingface.copaperAdded 31 Oct 2026π§ AI
WorkFactory: Turning Professional Intents into Executable Work for Scalable Agent TrainingWorkFactory is a framework that builds coherent, executable professional tasks for training working agents. It plans an information support graph from professional intents, then uses probe executions to find and repair missing or inconsistent task information. The authors built 112,686 tasks across 612 occupations and fine-tuned Qwen models at 4B, 27B and 35B-A3B (WorkPilot). WorkPilot-35B-A3B rose from 879 to 1565 Elo on GDPval-AA v2.1.
huggingface.copaperAdded 30 Oct 2026π§ AI
GPT-Rosalind-6 System Card AddendumGPT-Rosalind-6 System Card Addendum is OpenAI's safety documentation for a life-sciences model that shares weights with GPT-6 Astra. It rates the model High in Biological and Chemical and Critical in Cybersecurity under the Preparedness Framework. It reports biology evaluations, trusted-access controls, biosecurity refusal pass rates, a historical cyber jailbreak regression (14.3% task success), and sabotage and monitor-evasion results.
deploymentsafety.openai.compaperPublished and added 30 Oct 2026π‘οΈ Security- GPT-Rosalind-6 System Card Addendum
GPT-Rosalind-6 System Card Addendum describes OpenAI's life-sciences reasoning model, which uses the same weights as GPT-6 Astra and is offered only through trusted access. It reports life-science benchmark gains over GPT-Rosalind-5.5 (MedChemBench v7 49.6% vs 28.9%), High-capability Biological and Chemical Preparedness results, and below-threshold Critical bio design evaluations. It also covers access controls, refusal evaluations, cyber jailbreak regression and alignment tables.
deploymentsafety.openai.compaperPublished and added 30 Oct 2026π§ AI 
world2vec: Mechanistic world models through latent dynamics learningworld2vec is a method for learning mechanistic world models from observations and actions alone, without reward information. Its latent dynamics reproduce the true system over long prediction horizons using at least one order of magnitude fewer parameters than DreamerV3, TD-MPC2 and LeWM. The authors report state-of-the-art control of continuous environments from pixels and accurate system identification across a benchmark of 71 attractors.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ AI- TextSeal2: Distortion-Free LLM Watermarking with Copula Mixing and Memoryless Races
TextSeal2 is a distortion-free LLM text watermark that merges two or more keys into one virtual key and couples it to fresh noise through AutoMark's Gaussian copula. Under speculative decoding it verifies keyed drafts with an extra exponential race, so output matches the target model and acceptance rate is unchanged. On three LLMs at a 10^-3 false-positive rate, it detects 8 to 22 points more 100-token answers than TextSeal.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π‘οΈ Security - TextSeal2: Distortion-Free LLM Watermarking with Copula Mixing and Memoryless Races
TextSeal2 is a distortion-free watermark for LLM output that keeps its signal under speculative decoding. It merges two or more keys into one virtual key coupled to fresh noise through AutoMark's Gaussian copula, and verifies drafts with an extra exponential race. On three LLMs it detects 8 to 22 points more 100-token answers than TextSeal at a 10^-3 false-positive rate.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ AI - RADIOlogy: A Single-Pass Approach to Training-Free Zero-Shot Anomaly Detection
RADIOlogy is a training-free, single-pass zero-shot anomaly detection method that uses multi-head attention pooling in vision-language models. It scores anomalies by contrasting average similarity between vision features and sets of normal and abnormal prompts, with no windowed execution. RADIO agglomerative features drive a dense refinement pipeline for pixel-level segmentation, and the method is applied to medical images.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ AI - VeriCUA: Verification as Agentic Exploration for Computer-Using Agents
VeriCUA is a platform-agnostic framework that verifies task completion by computer-using agents through agentic exploration. The verifier inspects context to build a task rubric, then picks GUI or code checks for unresolved requirements and combines findings under explicit evidence rules. It works online and offline across desktop, mobile and web, and agrees better with reference labels than the baselines, with the largest offline gains on hard cases.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ AI - Multi-View MoE: Letting Every Router Shape Expert Computation
Multi-View MoE is a sparse mixture-of-experts design that uses several independently initialized routers to select experts, then reuses their relative scores to mix learned latent views into each selected expert's input. Each expert still runs one forward pass, so active-expert count, FLOPs and all-to-all volume are unchanged. On a 24-layer 10B-A1B model trained on 708B tokens, it gains 1.18 points on a nine-task mean; Multi-Router alone gains 0.6.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ AI - On the Fundamental Limits of Prefill-Based Decoding Length Prediction
This paper derives bounds on predicting an LLM's decode length from prefill-phase hidden states, a primitive for inference scheduling. It models decoding as a stopped stochastic process, giving per-prompt normality and an affine variance law Ο=Ξ±ΞΌ+b. Predictors (MLP, EGTP, FiLM, soft-label, Mixture-of-Experts) converge near the bound, so system error dominates model error. It proposes fixing model, data and sampling in benchmarks and reporting relative-error metrics.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ AI - Efficient Scalable Oversight via Rewinding
Rewindable interactive proofs (RIPs) are a new model of relativizing interactive proofs for scalable oversight, in which a weak verifier can rewind an AI prover to an earlier saved state. The paper builds an RIP for computations robust to changes in a small fraction of oracle answers. The honest prover runs in linear time, the verifier runs in nearly linear time in the input length after one-time preprocessing, and no cryptography is used. A preliminary MNIST experiment shows an RIP can be faster than a deployed proof system.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ AI - PhysCount: Do MLLMs Count Visual Appearances or Physical Entities?
PhysCount is a benchmark of 546 real-world images and 4,595 human-written questions in 651 target-specific groups. It tests whether multimodal LLMs can separate visible appearances (direct view, reflections, glass) from the physical entities behind them. Across 40 MLLMs, counting appearances is much easier than linking them to entities, and the best model answers every question correctly in only 13.36% of groups.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ AI - Glance: The Generator Is Its Own Reranker
Glance is a RAG method in which the generator also reranks. It adds a lightweight decision adapter and pointer head that score candidate passages from one shared prefill, so a single resident 9B backbone does generation and reranking. After 39 minutes of MS MARCO fine-tuning it reaches nDCG@10 of 0.741 on SciFact and 0.345 on NFCorpus, against 0.721 and 0.326 for bge-reranker-v2-m3. On 300 HotpotQA questions it improves answer F1 by 0.045 over cross-encoder-selected context. Evaluation covers two retrieval datasets, one QA dataset and one backbone family.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ AI - Retrieving from Your Own Mistakes: On-Policy Training of Embedding Models for Multi-Hop Retrieval-Augmented Generation
On-policy state training is a method for training chain embedding retrievers for multi-hop RAG on the states the encoder itself reaches, including those built on its own wrong retrievals. Each state is labeled with the earliest missing gold passage, other missing gold passages are masked, and updates are contrastive and mixed with teacher-forced states. It retrieves complete evidence more often than teacher-forced training and graph-based systems, and transfers to harder long-chain HotpotQA and 2WikiMultiHopQA variants the authors built.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ AI - Understanding and Mitigating Role Spoofing in LLMs
Understanding and Mitigating Role Spoofing in LLMs is an arXiv paper (October 2026) on role spoofing, a prompt injection where malicious tool output impersonates the user. It shows models track "who said what" using both content cues (semantics, style) and structure cues (control tokens, formatting). Attacks combining fake structure and content succeed often, adversarial training stops only content-based attacks, and linear probes trained to predict conversation roles detect spoofing across settings.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π‘οΈ Security - Agentic LLM reasoning in pediatric diabetes outcome prediction from multimodal medical records
CEDAR (Clinical Evidence-Driven Agent for Risk-prediction) is an evidence-citing, self-auditing LLM pipeline for predicting pediatric diabetes outcomes at 24 months from 18 months of records. On 2,489 youth with type 2 diabetes and 4,018 with type 1, pooled AUROC was 0.815 versus 0.656 to 0.700 for trained ML in T2D, and 0.756 versus 0.603 to 0.631 in T1D. Only 4 of 70,246 cited values mismatched the chart. Two pediatric endocrinologists matched its predictions in about nine of ten cases.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ AI - The Athlete as a Scientist of One: Agentic Bayesian Experimental Design for Individualized Physiology from Consumer Wearables
EpiCoach is an agentic system that treats one cross-country mountain biker as an N-of-1 experiment. Claims live in an evidence-gated hypothesis registry, and each must make a probabilistic prediction before a session. Predictions come from a digital twin fitted to heart rate, GPS and altitude. In a replay over eighteen months the twin beat standard forecasters on heart rate but not segment time, and the agent loop matched a static model. In the first three live weeks, the ledger sealed 20 predictions and scored 14, and a safety officer vetoed the agent twice. The registry, benchmark and protocol are released.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ AI - Don't Lose the Thread: Empowering Long-Horizon LLM Agents with Cognitive Resource Self-Allocation
CORAL (Cognitive Resource Self-Allocation) is an agent-callable working-memory toolset that lets an LLM agent checkpoint task progress and verified facts, then reset its cluttered context and resume from a clean state. A Multi-episode Agentic Reinforced Policy Optimization algorithm trains the checkpointing. With an 8B model it reaches 42.8% average accuracy on GAIA, uses 6-7x fewer input tokens than a ReAct agent, and adds 8.0 points to a proprietary agent on BrowseComp.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ AI - A Unified Hessian-Guided Framework for Post-Training Quantization
This preprint proposes a unified post-training quantization framework for large language models. It derives a Hessian-guided approximation of the output change caused by joint weight and activation quantization, using a tractable Fisher approximation. Quantization ranges, linear transformations and weight rounding are optimized together under one output-sensitivity objective. The authors report gains over state-of-the-art transformation and rounding methods in channel-wise and block-wise settings.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ AI - Decompose, Estimate, Select: Coverage-Aware Context Construction for Long-Form RAG
Decompose, Estimate, Select is a coverage-aware retrieval pipeline for long-form RAG. It splits a query into subquestions, ranks candidates with a coverage-trained bi-encoder, predicts a document-by-subquestion coverage matrix with a cross-encoder, and picks documents by greedy maximum-marginal coverage. On TREC RAG 2024, Ξ±-nDCG@10 rises from 0.625 to 0.702 (gold-nugget bound 0.718). A grounded self-feedback loop raises V_strict by 0.086 over two passes.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ AI - MAServeBench: Benchmarking Multi-Agent LLM Serving Systems with Hierarchical Collaboration Workloads
MAServeBench is a benchmark that treats multi-agent collaboration as a configurable dimension of LLM serving workloads. Its composable stages model how agents coordinate and exchange context in documented multi-agent workflows, and Hierarchical Semantic Traces record operations and dependencies for reproducible comparisons. Task-linked measurements tie workloads to GPU/NPU behavior, showing serving efficiency does not always mean timely task completion. Code is released.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ AI - Only Accepted Tokens Count: An Energy Accounting of Speculative Decoding Under Latency Targets
This paper measures the energy cost of speculative decoding in SGLang with CUDA graphs on H100 and H200 GPUs, at batch sizes up to 64 across 5 corpora. Savings depend on drafted-token acceptance, which is lower on conversation than code and lower on Romansh than English. Savings shrink as batch size grows, and block drafters can push verification into the compute-bound regime, where speculation raises energy per token.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ AI - Benchmarking color judgment in multimodal LLMs with CIE-computable ground truth
A five-task benchmark tests multimodal LLM color judgment against CIE colorimetric ground truth: AWB pair judgment, skin-tone bias direction, CCT estimation, Delta E 2000 pair ordering, and color naming under changing illuminants. Eight endpoints were tested. On ColorChecker-24 stimuli, closed frontier models reach CCT MAE of 715-1056 K versus 1358-2133 K for open-weight models, and a non-ColorChecker control shows this gap is partly template-dependent.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ AI - Turing LLM Post-Training: Calibrated Distillation and Efficient Dynamic-MoE Execution
Turing LLM post-training combines calibrated multi-teacher on-policy distillation (C-MOPD) with an execution path for dynamically routed mixture-of-experts models. C-MOPD matches each teacher on a local Top-K support with teacher-wise loss normalization and clipping. On Turing-20B-A2B it scores 0.9988 on four-domain integration, beats Qwen3-8B on math and code (85.92 vs 80.33 mean), and cuts consolidation step time from 398.7 to 139.3 seconds in a PPU profile.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ AI - M-PFT: Angular Margin for Calibrated Prompt-Tuning of Fine-Tuned Vision-Language Models
M-PFT is an angular-margin method for prompt tuning of vision-language models that fixes base-to-novel dual miscalibration (underconfidence on base classes, overconfidence on novel classes). It adds two losses: minimizing the maximum cosine similarity between tunable class embeddings, and minimizing angular distance to each zero-shot embedding. Tests on 9 prompt-tuning methods and 15 datasets show lower novel-class calibration error at comparable accuracy and negligible overhead.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ AI - Aligning Medical Vision Language Models at the Capability Boundary with Entropy Adaptive Rubric Grounding
MedCBR is a two-stage alignment framework for medical vision language models. Difficulty Weighted Boundary Preference Optimization (DBPO) trains on counterfactual preference pairs at the model's capability frontier. Entropy Gated Grounded Policy Optimization (EGPO) then applies distilled clinical rubrics according to prediction entropy. It reaches 61.2% average accuracy on six medical benchmarks, with the largest gains on differential diagnosis, and the two stages combine super-additively.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ AI - SEAL: Sealed Queries for Local Sanitization against Indirect Prompt Injection
SEAL is a defense against indirect prompt injection for local LLM agents. It learns a soft prompt and scoring readout offline, then uses a frozen task model to score and delete injected tokens from tool outputs, with queries built from trusted initial context. On AgentDojo, attack success falls from 34.6% to 0.1% and Joint rises from 51.4% to 77.5%, using about 127x fewer defense tokens than PISanitizer.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π‘οΈ Security - RetroChessBench: A Benchmark for Reconstructing and Proving Chess Histories
RetroChessBench is a 12-task benchmark for retrograde chess: reconstructing legal game histories, exact-length constructions, unique endings and exhaustive answer sets, with references, rubrics and a terminal-reward RL environment. GPT 6 Astra High fails to give the required final answer in 22 of 24 attempts without tools and 18 of 24 with code execution. Mean terminal RL rewards are 14.75% and 27.19%. The authors link the failures to scarce worked derivations in web training data.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ AI - Sparse Autoencoders as Implicit Diversification Regularisers for Cross-Sectional Cryptocurrency Returns
This arXiv preprint (October 2026) tests a sparse autoencoder as a factor model for daily cryptocurrency returns, with equities as a secondary test. SAE portfolios show consistently lower volatility than common benchmarks, linked to lower correlations among selected assets. They do not beat a simple contrarian strategy on risk-adjusted return, and factor identities change between training runs.
arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026π§ AI
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